Loading patent…
OwnerNick Hughesinventor
Estimating the term…
The AI-powered system for monitoring joint health is an innovative software-based medical device designed to transform arthritis care by addressing the limitations of traditional methods. Unlike conventional approaches that rely on subjective clinical evaluations and lengthy in-person consultations, this system utilises AI-driven algorithms to deliver real-time, objective assessments. Its unique ability to combine patient-reported outcomes with precise motion analysis sets a new standard for accuracy and efficiency in arthritis management. Utilising cameras on smartphones, tablets, or IoT devices, the system captures video frames or static images portraying a patient's joints, which are analysed by advanced machine learning algorithms. These algorithms detect joint positions, joint angles, range of motion, swelling, and skin discolouration, providing a comprehensive assessment of disease indicators. This comprehensive evaluation is achieved through machine learning models trained on extensive, clinically validated datasets. By leveraging these datasets, the system ensures high accuracy and reliability in its analysis, offering robust insights into the patient's condition. The system integrates this objective data with patient-reported outcomes to calculate a Digital Disease Activity Score (DDAS) that mirrors clinical standards like DAS-28, a benchmark recognised for its accuracy in assessing arthritis severity. By aligning with DAS-28, the system ensures compatibility with established clinical practices and enhances its utility in guiding treatment decisions. This integration supports precise monitoring of disease progression and facilitates personalised care recommendations, ultimately improving patient outcomes. Equipped with a user-friendly interface and secure cloud integration, the platform ensures compliance with data protection regulations while facilitating seamless data sharing between patients and clinicians. The interface provides intuitive navigation, clear instructions, and multilingual support, enhancing accessibility for diverse patient groups. Secure cloud integration adheres to stringent standards such as GDPR, DPA, and HIPAA, safeguarding sensitive patient data through robust encryption protocols and secure data transfer mechanisms. By enhancing accuracy, scalability, and accessibility, the system significantly improves patient outcomes and reduces healthcare burdens in arthritis management. This system is an assistive monitoring tool, not a diagnostic device, and is intended to provide objective assessments to support clinical decision-making rather than replace formal medical evaluations.
A method for remotely capturing and analysing joint positions using video data or static images obtained from a camera-equipped device, including but not limited to smartphones, tablets, IoT devices, wearable devices, or any future camera equipped apparatus capable of capturing joint movements, joint swelling or discolouration, wherein the method comprises:
An integrated system for evaluating joint health and disease severity in arthritis patients, comprising:
1. The method of claim 1, further comprising the detection of skin discoloration and swelling through the analysis of visual markers in video frames or static images.
2. The method of claim 1, wherein the extracted data is compared against clinically established predefined static thresholds derived from extensive clinical measurements, to identify deviations from healthy joint ranges, thereby enabling precise and reliable assessments of disease activity.
3. The method of claim 1, wherein the AI-driven algorithms, developed in Python, are trained on extensive, clinically validated datasets to ensure high accuracy and reliability in joint health analysis, leveraging advanced techniques to consistently deliver precise assessments.
4. The method of claim 1, wherein the AI-driven algorithms comprise convolutional neural networks (CNNs), and random forest models trained on datasets of annotated extracted joint data, images and videos of joints, wherein the datasets are continuously updated with new patient data to improve model accuracy and adaptability.
5. The method of claim 1, wherein the AI-driven algorithms are trained using supervised learning techniques, with labelled data, to detect joint positions, angles, range of motion (ROM), and visual indicators of arthritis, including swelling and skin discoloration.
6. The method of claim 1, wherein the AI-driven algorithms are configured to perform real-time processing of video and image data locally (when used through the downloaded app) on the camera-equipped device using edge computing techniques, enabling low-latency analysis of joint movements and immediate feedback to patients and clinicians. Alternatively, when accessed via a web browser, the system securely transmits data to cloud servers for processing, ensuring scalability and consistent performance across platforms.
7. The method of claim 1, wherein the system employs edge computing to process video and image data locally on the device, reducing the need for cloud-based processing and ensuring efficient analysis in low-bandwidth environments.
1. The system of claim 2, further comprising a user interface that provides multilingual, intuitive instructions for patient assessments and offers clinicians analytics for reviewing assessment data.
2. The system of claim 2, wherein data handling is enhanced through secure cloud integration, ensuring compliance with DPA, GDPR and HIPAA for safe data storage, transfer, and processing. All data is encrypted both in transit (via HTTPS) and at rest, ensuring that it cannot be intercepted or accessed by unauthorised users during transmission or storage.
3. The system of claim 2, wherein the DDAS mechanism incorporates weighted computations aligned with clinical standards for a nuanced analysis of disease activity. These computations combine objective data from joint range of motion and patient-reported indicators, enabling a holistic evaluation of arthritis severity. By integrating thresholds established through validated pose estimation models, the system ensures precise and reliable assessments that directly inform personalised treatment plans and clinical decision-making.
4. The system of claim 2, further comprising advanced features such as flare-up reporting, progress tracking, and integration with electronic health record (EHR) systems for streamlined clinical workflows.
Dependencies are inferred from the claim text.